Dimension Labs

Industry

Customer feedback analytics for retail

Read every review, chat and return reason across stores and online. Find which issue actually moves rating, returns and repeat purchase.

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Ask Which issue drives returns and lost repeat purchase?

We work with enterprises such as NBA, Travelers, Mars and Concentrix.

[01] The problem

Returns tell you what came back. Customers tell you why.

Retail feedback is spread across store reviews, support chats, marketplace ratings and return codes. Each team reads its own slice, so nobody sees which issue costs the most.

Right now

01Store and online feedback live in different tools
02Return codes hide the real reason
03Delivery failures surface as one-star reviews weeks later
04Store comparisons stall on inconsistent coding
05Merchandising decisions ignore what customers wrote

Every return reason and every review is a vote. Nobody is counting them on one scale.

[02] EnrichOne record, structured

Language in. Structured data out.

Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for retail and e-commerce.

enrichment · one record

Raw text

Tracking says delivered on Tuesday but nothing is at my door. I've tried the chat three times and nobody answers. Just refund me, I'll buy it somewhere else.

Chat transcript · online order · order joined

Dimensions written to the record

Order & Service Accuracydelivered, not received
Effort Levelhigh · 3 attempts
Resolution Statusunresolved
Return & Refund Signalrefund requested
Churn & Cancellation Riskhigh
Competitor Mentionedimplied

Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side.

[03] The product

3.1 · Read everything

Every record, enriched.

Start with one export of store reviews, support chats, return reasons and product reviews, or connect every channel. Every record comes back with typed Dimensions like order and service accuracy, return and refund signal, effort level, location referenced and price-value perception, written the same way every run, then joined to store, SKU, order and loyalty data.

Read every record, not a sample
Join each record to store, SKU, order and loyalty data
Add your own Dimensions in plain English
Backfill years of history in the same run

dimension labs · blend

3.2 · Prove the cause

Ask why. Get proof.

Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, returns and repeat purchase, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which issue drives returns and lost repeat purchase
Compare every store on the same Dimensions
Find delivery failures before they hit ratings
See which products customers regret buying

dimension labs · ask

Example finding

Which issue drives returns and lost repeat purchase?

See the Home Depot multi-location review analysis: 19 Dimensions across public reviews, compared store by store.

Published artifact · public reviews

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your store operations and e-commerce leads needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor.

Produce the monthly readout without building it
Publish a live monitor leadership can open and question
Track whether the fix you shipped moved the number
Export to PDF, slides or your BI tool

dimension labs · report

[04] How the platform worksRuns daily

From raw conversations to a proven answer.

Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works.

Language · 37 sources

ZendeskIntercomSalesforceLiveChatDriftSlackQualtricsMedalliaSurveyMonkeyQuestionProGenesysAmazon ConnectAvaya Cloud8x8TwilioSMSAdaKore.aiYellow.aiCognigyRasaMicrosoft Bot FrameworkAmazon LexAmazon AlexaOracle Digital AssistantBotcopyCapacityFacebook MessengerKikX (Twitter)OpenAIEmailCall recordingsApp store reviewsGoogle reviewsOpen-ended surveysCommunity forums

Business + world data

SnowflakeDatabricksBigQueryAmazon S3Salesforce CRMCSV + ExcelREST APIRevenueOrdersRenewalsUsage eventsLoyaltyStore + location dataWeatherSeasonalityMarket events

Dimension Labs pipeline · Runs daily

Ingest + normalizeEvery channel and language into one schema

EnrichDozens of Dimensions written onto every record

Map + governValidated for drift, versioned, the same fields every run

Analyze + proveJoined to outcomes and tested for cause

Where it goes

Dimension Labs agentAsk, report, schedule

Your warehouseSnowflake, Databricks, BigQuery

BI toolsTableau, Power BI, Hex

Claude and ChatGPTvia MCP · coming soon

[05] ArtifactsBuilt by the agent